Literature DB >> 15714640

Progress in understanding and using over-the-counter pharmaceuticals for syndromic surveillance.

Steven F Magruder1, S Happel Lewis, A Najmi, E Florio.   

Abstract

INTRODUCTION: Public health researchers are increasingly interested in the potential use of monitoring data on over-the-counter (OTC) pharmaceutical sales as a source of timely information about community health. However, fundamental uncertainties persist, including how timely such information is and how best to aggregate information about hundreds of products.
OBJECTIVES: This analysis provides new information about OTC timeliness and illustrates a method of OTC product aggregation for surveillance purposes.
METHODS: Timeliness measurements were made by correlating pharmaceutical sales counts with counts of physician encounters, after adjustment to remove seasonal effects from both counts. OTC product aggregations were formed by a two-stage process. In the first stage, individual products were placed into small groups based on qualitative observations. In the second stage, a clustering algorithm was used to form supergroups (i.e., product group clusters) sharing similar sales histories.
RESULTS: Even after seasonal correction, OTC counts correlated with clinical measures of community illness. However, the lead time of nonseasonal fluctuations was substantially shorter than that for uncorrected data. The clustering approach produced 16 meaningful supergroups containing products that behaved approximately alike.
CONCLUSIONS: Measurements of OTC lead time sensitive to the timing of annual cyclic trends in the behavior of persons seeking health care do not reliably indicate the lead time observed for short-term (e. g. weekly or monthly) fluctuations in community health-care utilization.

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Year:  2004        PMID: 15714640

Source DB:  PubMed          Journal:  MMWR Suppl        ISSN: 2380-8942


  18 in total

1.  A multivariate procedure for identifying correlations between diagnoses and over-the-counter products from historical datasets.

Authors:  Ran Li; Garrick L Wallstrom; William R Hogan
Journal:  AMIA Annu Symp Proc       Date:  2005

Review 2.  Review of syndromic surveillance: implications for waterborne disease detection.

Authors:  Magdalena Berger; Rita Shiau; June M Weintraub
Journal:  J Epidemiol Community Health       Date:  2006-06       Impact factor: 3.710

3.  Finding leading indicators for disease outbreaks: filtering, cross-correlation, and caveats.

Authors:  Ronald M Bloom; David L Buckeridge; Karen E Cheng
Journal:  J Am Med Inform Assoc       Date:  2006-10-26       Impact factor: 4.497

4.  Factors associated with the use of over-the-counter medications in cases of acute gastroenteritis in Hamilton, Ontario.

Authors:  Gillian O Frosst; Shannon E Majowicz; Victoria L Edge
Journal:  Can J Public Health       Date:  2006 Nov-Dec

5.  Unsupervised clustering of over-the-counter healthcare products into product categories.

Authors:  Garrick L Wallstrom; William R Hogan
Journal:  J Biomed Inform       Date:  2007-04-03       Impact factor: 6.317

6.  Using encounters versus episodes in syndromic surveillance.

Authors:  I Jung; M Kulldorff; K P Kleinman; W K Yih; R Platt
Journal:  J Public Health (Oxf)       Date:  2009-05-13       Impact factor: 2.341

7.  A Practitioner-Driven Research Agenda for Syndromic Surveillance.

Authors:  Richard S Hopkins; Catherine C Tong; Howard S Burkom; Judy E Akkina; John Berezowski; Mika Shigematsu; Patrick D Finley; Ian Painter; Roland Gamache; Victor J Del Rio Vilas; Laura C Streichert
Journal:  Public Health Rep       Date:  2017 Jul/Aug       Impact factor: 2.792

8.  Mining aggregates of over-the-counter products for syndromic surveillance.

Authors:  Aurel Cami; Garrick L Wallstrom; Ashley L Fowlkes; Cathy A Panozzo; William R Hogan
Journal:  Pattern Recognit Lett       Date:  2009-02-01       Impact factor: 3.756

9.  Unsupervised clustering of wildlife necropsy data for syndromic surveillance.

Authors:  Eva Warns-Petit; Eric Morignat; Marc Artois; Didier Calavas
Journal:  BMC Vet Res       Date:  2010-12-16       Impact factor: 2.741

10.  Combining free text and structured electronic medical record entries to detect acute respiratory infections.

Authors:  Sylvain DeLisle; Brett South; Jill A Anthony; Ericka Kalp; Adi Gundlapallli; Frank C Curriero; Greg E Glass; Matthew Samore; Trish M Perl
Journal:  PLoS One       Date:  2010-10-14       Impact factor: 3.240

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